In this project, I wanted to analyze migration patterns in the Bay Area, which led me to questions of housing and housing prices as related to income concentration.

I also wanted to assess demographics in the Bay Area and examine different equity analyses and indices of segregation. Finally, I related all of these contemporary findings to the legacy of historic redlining and economic risk “grades” given to different Bay Area regions.

Datasets for this project

For this project, I use various different types of census data as well as data from the 1937 Home Owners’ Loan Corporation grading scheme of American neighborhoods.

Every year, the U.S. Census Bureau puts out and publishes the American Communities Survey (ACS) 1-year estimates for geographic areas with populations of 65,000 or more. This data is taken from random households and asks more detailed questions than the decennial census because its purpose is to track various economic and social changes rather than broad population counts. The ACS 5-year estimates are a bit more statistically reliable than the 1-year estimates because they gather data over consecutive years and also have larger sample sizes.

I use both American Communities Survey 1-year and 5-year data. In particular, 1-year estimates are helpful for understanding quickly changing geographic or demographic data, such as mobility or in-flow/out-flow data. 5-year estimates can provide interesting insights into broader trends in areas such as racial demographics or home values and annual income. Public Use Microdata Sample (PUMS) files are also included in these ACS reports. The PUMS data provides even more specific and granular data from individuals and households. This data is helpful for some of the more specific variables I measure, such as households with income less than 100K.

Bay Area Migration - 2019

These 3D Maps show the inflow and outflow for the Bay Area. The arcs show the top 50 inflow to and outflow from the Bay Area locations. To navigate these maps, double tap or scroll down with your mouse to zoom in and scroll up with your mouse to zoom out on particular spots.

Zoom in on the Bay Area to get a closer look at the top 50 destinations and places of departure.

Bay Area Inflow Locations - 2019

This map uses the 2019 ACS 1 year estimates to show the inflow of people moving to the Bay Area from the top 50 inflow origin locations.

Bay Area Outflow Locations - 2019

This map uses the same data as the previous map to show the top 50 outflow locations for people moving out of the Bay Area.

In comparing these two maps, a lot of the 2019 inflow and outflow locations are very similar, such as moving to or from San Francisco. There is more geographic distance in the inflow destinations, with more people moving from big cities like New York and Chicago. The outflow destinations are closer to the Bay Area, with people moving to places like cities in Southern California or Las Vegas in Nevada (where housing prices and taxes are much lower but still close enough to potentially work remotely or stay near family).

Bay Area Flows by Income Tier

## # A tibble: 9 × 7
##   `Income tier`  `Internal net` `External net` `Here last year` `Here this year`
##   <chr>                   <dbl>          <dbl>            <dbl>            <dbl>
## 1 $1 to $9,999 …         -35060          -5288           748444           708096
## 2 $10,000 to $1…         -28127           2009           423540           397422
## 3 $15,000 to $2…         -34457          -4504           605718           566757
## 4 $25,000 to $3…         -15978          -3686           524112           504448
## 5 $35,000 to $4…          23731          -2252           635373           656852
## 6 $50,000 to $6…           2244          -2817           546202           545629
## 7 $65,000 to $7…           5978           1565           275541           283084
## 8 $75,000 or mo…         104138          11355          1854093          1969586
## 9 No income              -22232          10273           823693           811734
## # … with 2 more variables: Outflow <dbl>, Inflow <dbl>

This table shows inflow and outflow across the Bay Area counties based on income tiers. This data comes from the ACS 1-year estimates of Geographic Mobility from 2018 to 2019. Those with a higher income have more inflow than outflow to the Bay Area, and those with lower incomes have more outflow than inflow to the Bay Area. These flows based on income tier make sense with increasing housing costs and taxes in the Bay Area.

Assesing Bay Area Income and Housing

Median Home Values by Bay Area County - 2019

This is a map of the median home values in the Bay Area counties 2019 using the ACS 1 year estimates. It shows San Mateo ($1,233,600) with the highest median home values, then San Francisco County ($1,217,500), then Santa Clara ($1,135,600), Marin ($1,078,800), Alameda ($882,100), Contra Costa ($687,600), Napa ($670,000), Sonoma ($664,600), and Solano ($460,500).

This is interesting to compare to the inflow and outflow maps. The regions with the most inflows and outflows such as San Mateo, San Francisco, and San Jose are also some of the most expensive places to have homes on average.

Look at the plot above to see the changes in Median Home Value across the Bay Area Counties from 2005 to 2019. You can see the dip in values from the 2008 housing and financial crisis, but after around 2012 median housing prices began to increase quickly and continue to increase.

Median Annual Household Income by County - 2019

This is a map of the median home annual income in the Bay Area counties 2019 using the ACS 1 year estimates. The highest median annual income is in Santa Clara County ($124,055), then San Mateo ($122,641), San Francisco ($112,449), Marin ($115,246), Contra Costa ($99,716), Alameda ($99,406), Napa ($88,596), Solano ($81,472), and Sonoma ($81,018).

This order is relatively similar to that of the median housing prices for 2019. The counties with the highest income seem to have the highest rates in-flow and out-flow as well, if you recall the early section.


If we look at the increase in median home prices according to the ACS 1 year survey, we can see that housing prices have increased with median income levels for each county. The years after the 2008 Housing and Financial Crisis marked a significant dip in both sets of data, but after 2012 both values began to rise and kept rising.

Bay Area Racial Demographics by County

To better understand and work to address housing and income inequality it is critical to address and unpack racial disparities and inequality. Racial disparities are a painful and ever-present reality as a result of systemic and historic inequities and discrimination that are perpetuated by policies of the past and present.

First lets take a look at Bay Area racial demographics by county using the census data.

Dot Density Map of Concentration of Different Racial Groups in the Bay Area - 2019 (ACS)
This map uses dot to represent the concentration of various census racial categories over a map of the Bay Area (ACS 2019).

Racial Equity Analysis

Equity analyses are important to conduct in order for policy makers, researchers, and community organizers to highlight communities that are under-served and allocate policies, funding, or advocacy to help address these inequities.

Household Income Distribution by Race - 2019

This plot shows the proportions of Bay Area Household Income by Race with the American Communities Survey (ACS) 5 year data. This plot shows that “White Alone” households have the highest proportion of income across all income brackets but especially for the total, then “Asian Alone” and the lowest proportions are “American Indian and Alaska Native Alone” and “Native Hawaiian and Other Pacific Islander Alone.”

Geographic Mobility in the Past Year by Household - 2019

This plot shows the proportions of Bay Area Household Mobility by Race for with the American Communities Survey (ACS) 5 year data. “White Alone” households have the highest proportion of geographic mobility in all categories except “moved from abroad” in which “Asian Alone” households have the highest proportion. “American Indian or Alaska Native Alone” and “Native Hawaiian and Other Pacific Islander Alone” have the lowest proportion of geographic mobility for every category (besides “Some Other Race Alone”).

The geographic mobility proportions are similar to the ordering of household income by race. While both plots show significant disparities, interestingly, there is more equity or equal distribution in the geographic mobility analysis.

Check out part two to see some broader trends in Bay Area racial demographics as linked to housing and economic equity and historical polices of dicrimination.